Instructions to use google/gemma-4-E2B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-E2B-it with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E2B-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
Commit History
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Raise per-image vision soft-token budget from 280 to 1120
Verified signatureIncreases the default per-image vision soft-token budget from 280 to 1120 (the maximum supported value) so images are encoded at higher resolution by default. Video budget is unchanged (`video_processor.max_soft_tokens` = 70); no other values are modified. Exact changes in this repo: - `config.json`: - `vision_config.default_output_length`: 280 → 1120 - `vision_soft_tokens_per_image`: 280 → 1120 - `processor_config.json`: - `image_processor.image_seq_length`: 280 → 1120 - `image_processor.max_soft_tokens`: 280 → 1120 - `image_seq_length`: 280 → 11208281b08
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Preparing for release!
Co-authored-by: Rocketknight1 <Rocketknight1@users.noreply.huggingface.co> Co-authored-by: RyanMullins <RyanMullins@users.noreply.huggingface.co> Co-authored-by: pcuenq <pcuenq@users.noreply.huggingface.co> Co-authored-by: bebechien <bebechien@users.noreply.huggingface.co> Co-authored-by: osanseviero <osanseviero@users.noreply.huggingface.co> Co-authored-by: douglas reid <douglas reid@users.noreply.huggingface.co>4742fe8